Hierarchical multi-tier language processing platform

ABSTRACT

Aspects of the disclosure relate to systems and methods for increasing the speed, accuracy, and efficiency of language processing systems. A provided method may include storing a plurality of modules in a database. The method may include configuring the plurality of modules in a multi-tier tree architecture. The method may include receiving an utterance. The method may include processing the utterance via a natural language processing (NLP) engine. The method may include routing the utterance. The routing may include identifying a highest tier module that matches a predetermined portion of the utterance. The method may include compiling a result set of modules. The method may include transmitting the result set of modules to the system user. The result set of modules may include a comprehensive and narrowly tailored response to the user request.

FIELD OF TECHNOLOGY

Aspects of the disclosure relate to computer systems. Specifically,aspects of the disclosure relate to computerized language processingsystems.

BACKGROUND OF THE DISCLOSURE

Language processing systems are useful for processing spoken utterancesand resolving the intent of the speaker of the utterance. Once resolved,a computer system may be able to respond appropriately to a request ofthe speaker when a request is included or implicated in the intent ofthe speaker.

Conventional language processing systems, however, often suffer fromspeed, accuracy, and efficiency deficiencies. It may be difficult forthe conventional systems to quickly disambiguate the utterance. Thisdifficulty may cause the system to produce slow and/or inaccurateresults. For example, if the word “apple” is a part of the utterance,the system may not be able to quickly determine if the intent is for thefruit or a company of the same name. Conventional systems may make anincorrect assumption. Conventional systems may take processing time toachieve an accurate result. Conventional systems may engage intime-consuming queries to the speaker to disambiguate the utterance andachieve an accurate result.

It would be desirable, therefore, to provide systems and methods forincreasing the speed, accuracy, and efficiency of language processingsystems.

SUMMARY OF THE DISCLOSURE

Aspects of the disclosure relate to a hierarchical multi-tier digitalplatform with increased processing speed, accuracy, and efficiency forrouting a verbal request. The platform may include a processor. Theplatform may include a non-transitory memory storing a set ofcomputer-executable instructions, that, when run on the processor, areconfigured to perform some or all platform functionality.

The platform may include a database including a plurality of modules.Each module may include a set of information. The plurality of modulesmay be configured in a multi-tier tree architecture. The multi-tier treearchitecture may include at least three tiers. The first tier mayinclude a first module that is a root node. Each tier aside from thefirst tier may include one or more modules that are each a child of aparent module that is one tier up in the tree (i.e., closer to the firsttier). The set of information of a child module may be a subset of theset of information of the parent module of said child module.

The platform may be configured to receive an utterance. An utterance mayinclude a string of one or more words spoken by a system user. Thestring of words may include a user request. The platform may beconfigured to process the utterance via a natural language processing(NLP) engine.

The platform may be configured to route the utterance. Routing theutterance may include identifying a highest tier module that matches apredetermined portion of the utterance. When at least one descendentmodule of the highest tier module exists, the platform may be configuredto compile one or more of the descendent modules into a result set ofmodules. When descendent modules of the highest tier module do notexist, the platform may be configured to compile the highest tier moduleinto the result set of modules. The platform may also be configured totransmit the result set of modules to the system user. The result set ofmodules may include a comprehensive and narrowly tailored response tothe user request.

BRIEF DESCRIPTION OF THE DRAWINGS

The objects and advantages of the disclosure will be apparent uponconsideration of the following detailed description, taken inconjunction with the accompanying drawings, in which like referencecharacters refer to like parts throughout, and in which:

FIG. 1 shows an illustrative system in accordance with principles of thedisclosure;

FIG. 2 shows an illustrative apparatus in accordance with principles ofthe disclosure;

FIG. 3 shows an illustrative system architecture in accordance withprinciples of the disclosure;

FIG. 4 shows an illustrative system diagram in accordance withprinciples of the disclosure; and

FIG. 5 shows an illustrative flowchart in accordance with principles ofthe disclosure.

DETAILED DESCRIPTION OF THE DISCLOSURE

Aspects of the disclosure relate to systems and methods for increasingthe speed, accuracy, and efficiency of language processing systems.

A hierarchical multi-tier digital platform is provided. The platform maybe for routing a verbal or textual request. The platform may provideincreased processing speed, accuracy, and efficiency. The platform mayinclude a processor. The platform may include a non-transitory memorystoring a set of computer-executable instructions, that, when run on theprocessor, are configured to perform some or all platform functionality.

The platform may include a database. The database may include aplurality of modules. Each module may include a set of information. Eachmodule may correspond to an account or a category of accounts.

For example, in the context of an illustrative language processingsystem related to financial services, the plurality of modules may eachrepresent a financial services account or a category of accounts. Theset of information may include general account information. The set ofinformation may also include user-specific information relating to theaccount, such as whether a system user is subscribed to the account (oran account within the account category), and/or account balance,constitution, or any other suitable account information. The set ofinformation may include rules, such as regular expression (“regex”)patterns, for use in resolving utterances related to the account orcategory of accounts.

The plurality of modules may be configured in a multi-tier treearchitecture. The multi-tier tree architecture may include two tiers.The multi-tier tree architecture may, in certain preferred embodiments,include at least three tiers. The first tier may include a first modulethat is a root node. Each tier aside from the first tier may include oneor more modules that are each a child of a parent module that is onetier up in the tree (i.e., closer to the first tier). The set ofinformation of a child module may be a subset of the set of informationof the parent module of said child module.

For example, in the illustrative case of a financial services system,the root node may represent a category that includes all financialservices offered by the system. The second tier may includesubcategories such as a transactional account category and an investmentaccount category. The third tier may include subcategories of thesecond-tier categories. For example, the third tier may include asubcategory module representing retirement funds, which may in turn havedescendant fourth tier modules representing specific retirement fundsoffered by the system. The third tier may also include modules thatrepresent actual investment accounts, such as a certificate of deposit(CD) account. The tree structure may also include many other modules,some of which may represent categories—which in turn may have descendantmodules that represent categories and/or accounts—and some of which mayrepresent actual accounts.

The platform may be configured to receive an utterance. An utterance mayinclude a string of one or more words spoken by a system user. In someembodiments, the utterance may be a string of words typed, written, orotherwise generated by the user. The string of words may include a userrequest. The request may, for example, relate to an account representedby one or more of the modules.

The platform may be configured to process the utterance via a naturallanguage processing (NLP) engine. In some embodiments, processing theutterance may include tokenizing and/or annotating the utterance via theNLP engine. Tokenizing and/or annotating the utterance may includebreaking the utterance into words or phrases, and labeling thewords/phrases with metadata such as parts-of-speech labels.

The platform may be configured to route the utterance. Routing theutterance may include identifying a highest (i.e., closest to the firsttier) tier module that matches a predetermined portion of the utterance.The predetermined portion may include the whole utterance. Thepredetermined portion may include a keyword. The predetermined portionmay include all significant words in the utterance. The predeterminedportion may include any suitable portion that, when used to identify amatch, facilitates a meaningful match representing substantiallyequivalent intents.

When at least one descendent (i.e., a child, grandchild, etc.) module ofthe highest tier module exists, the platform may be configured tocompile one or more of the descendent modules into a result set ofmodules. When descendent modules of the highest tier module do notexist, the platform may be configured to compile the highest tier modulealone into the result set of modules. The platform may also beconfigured to transmit the result set of modules to the system user. Theresult set of modules may include a comprehensive and narrowly tailoredresponse to the user request.

In some embodiments, the result set of modules may include only thedescendent modules that are a maximum of one tier below said highesttier module.

In certain embodiments, when the result set of modules includes at leastone module that is a parent module to at least two child modules,systems and methods may be further configured to transmit to the systemuser a request to select one or more modules from the at least two childmodules, and, based on the selection, remove from the result set anyunselected modules of the at least two child modules.

In some embodiments, the result set of modules may include only modulesthat are leaf nodes. A leaf node may be a module that is not a parentmodule of any child modules.

In some embodiments, the system user may be subscribed to one or more ofthe plurality of modules. The result set of modules may be filtered toinclude only subscribed modules. Subscribed modules may include modulesto which the system user is subscribed or to which the system user issubscribed to at least one descendent.

In one illustrative scenario, the utterance may be “what is my accountbalance.” The highest tier that matches this utterance may be the firsttier, including all financial services. The result set may include allthe modules of the second tier. In a scenario where the second-tiermodules include at least two modules which have descendent modules inthe third tier, the system may prompt the user to select one or more ofthose modules. In another embodiment, the platform may not include anymodules that are categories, and may only include modules which areactual accounts, and these may be represented by modules of any tier.Furthermore, some embodiments may only include in the result set thoseaccounts to which the user is subscribed.

In another illustrative scenario, the utterance may be “show me myretirement account balance.” The highest tier module matching theutterance may be a retirement account category which may be representedby a module in the third tier. The retirement account module of thethird tier may, for example, have three child modules in the fourthtier, a Roth account module, a 401k module, and an Individual RetirementArrangements (IRA) module. The Roth account module may represent acategory, and may, for example, be associated with two child modules inthe fifth tier, a first and second Roth account. The 401k module and theIRA module may be leaf nodes that represent actual accounts.

In this scenario, the result set may include the three child modules,and may prompt the user for a selection. In some embodiments, the resultset may include the first and second Roth accounts and also the 401kaccount and the IRA account. In some embodiments, the platform mayautomatically exclude any account to which the user is not subscribed.For example, if the user has a second Roth account and a 401k, theresult set that is transmitted to the user may include only the secondRoth account and the 401k.

In certain embodiments, each module may include an investment account ora category of investment accounts. The set of information of a modulemay include investment account information.

In some embodiments, the database may further include at least one ruleset. A rule set may include a set of regular expression (“regex”)patterns. Systems and methods may be further configured to resolve anintent of the utterance by comparing the utterance to the rule set tofind a match between the utterance and one of the regular expressionpatterns.

In certain embodiments, the database may include a distinct rule set foreach module. Each distinct rule set may be tailored based on requeststypically received for the accounts and categories represented by eachmodule, thereby enabling quick and efficient resolution of the intentsof received utterances.

In some embodiments, one of the rule sets may include a rule foridentifying stock names. The rule for identifying stock names mayinclude identifying a word from the utterance that is identified by thesystem as a noun as a stock name. A word that is identified by thesystem as a noun may include a word that is a part of speech other thana noun but is used in a portion of the utterance that is associated withbeing a noun.

In certain embodiments, the set of regular expression patterns of one ofthe rule sets may include one, some, or all of the regex patterns below.These illustrative regex patterns may match requests that may typicallybe received within a certain module, such as an investment requestmodule.

([/range|quote|close|open/] [/for|of/]?)(?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/reports|price/] [/range/]?[/for|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/sector/][/for|to/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/52-week/] [/range/][/for|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/analysts/])(?$StockName[{tag:/NN.*|FW|VBP/}]{1,4}); ([/nav/])(?$StockName[{tag:/NN.*|FW|VBP/}]{1,4});([/peers|volume|esg|analysts|articles|beta|coverage|dividend|earnings|eps|nav|news|fundamentals|chart|chain/] [/for|to|of/]?)(?$StockName[{tag:/NN.*|FW|VBP/}]{1,4}); ([/cfra|lipper|morningstar/]?[/rating|ratings/] [/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4});([/day|day's/] [/'s/]? [/high|low|change/] [/for|to|of/]?)(?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/market/]? [/cap/][/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/price/][/objective/] [/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4});([/news/] [/wire/]? [/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4});([/expense|price|p\/e/] [/ratio/]? [/for|to|of/]?)(?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/earnings/] [/per/]? [/share/]?[/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/fund/][/inception/] [/date/] [/for|to|of/]?)(?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/carbon/] [/footprint/][/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/net/] [/asset/][/value/] [/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4});(?$StockName[{tag:/NN.*|FW/}]{1,4}) ([/quote|chart|trends|doing/]);(?$StockName[{tag:/NN.*|FW/}]{1,4}) ([/stock|fund/] [/story/]?);([/stock|fund|quote|chart|trends|impact/] [/for|to|of/]?)(?$StockName[{tag:/NN.*|FW/}]{1,4}); and ([/sell|buy|add|remove|short/][/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4}).

NN may refer to a word that is associated with being a noun. FW mayrefer to a foreign word. A foreign word may be a word that is recognizedas a word from a foreign language, or, in certain embodiments, a wordthat is not recognized by the system. VBP may refer to a singular verbin present tense.

A method for routing a verbal request with increased processing speed,accuracy, and efficiency is provided. The method may be executed via aset of computer-executable instructions stored in a non-transitorymemory and run on a processor. The method may include: storing aplurality of modules in a database, each module comprising a set ofinformation; configuring said plurality of modules in a multi-tier treearchitecture, said multi-tier tree architecture comprising at leastthree tiers, wherein: the first tier comprises a first module that is aroot node; each tier aside from the first tier comprises one or moremodules that are each a child of a parent module that is one tier up inthe tree, wherein up in the tree is closer to the first tier; and theset of information of a child module is a subset of the set ofinformation of the parent module of said child module.

The method may also include receiving an utterance. The utterance mayinclude a string of one or more words spoken by a system user. Thestring of words may include a user request. The method may also includeprocessing the utterance via a natural language processing (NLP) engine.The method may also include routing the utterance. The routing mayinclude: identifying a highest tier module that matches a predeterminedportion of the utterance; when at least one descendent module of saidhighest tier module exists, compiling one or more of the descendentmodules into a result set of modules; when descendent modules of saidhighest tier module do not exist, compiling said highest tier moduleinto the result set of modules; and transmitting the result set ofmodules to the system user, the result set of modules comprising acomprehensive and narrowly tailored response to the user request.

Apparatus and methods described herein are illustrative. Apparatus andmethods in accordance with this disclosure will now be described inconnection with the figures, which form a part hereof. The figures showillustrative features of apparatus and method steps in accordance withthe principles of this disclosure. It is understood that otherembodiments may be utilized, and that structural, functional, andprocedural modifications may be made without departing from the scopeand spirit of the present disclosure.

FIG. 1 shows an illustrative block diagram of system 100 that includescomputer 101. Computer 101 may alternatively be referred to herein as a“server” or a “computing device.” Computer 101 may be a workstation,desktop, laptop, tablet, smart phone, or any other suitable computingdevice. Elements of system 100, including computer 101, may be used toimplement various aspects of the systems and methods disclosed herein.

Computer 101 may have a processor 103 for controlling the operation ofthe device and its associated components, and may include RAM 105, ROM107, input/output module 109, and a memory 115. The processor 103 mayalso execute all software running on the computer—e.g., the operatingsystem and/or voice recognition software. Other components commonly usedfor computers, such as EEPROM or Flash memory or any other suitablecomponents, may also be part of the computer 101.

The memory 115 may be comprised of any suitable permanent storagetechnology—e.g., a hard drive. The memory 115 may store softwareincluding the operating system 117 and application(s) 119 along with anydata 111 needed for the operation of the system 100. Memory 115 may alsostore videos, text, and/or audio assistance files. The videos, text,and/or audio assistance files may also be stored in cache memory, or anyother suitable memory. Alternatively, some or all of computer executableinstructions (alternatively referred to as “code”) may be embodied inhardware or firmware (not shown). The computer 101 may execute theinstructions embodied by the software to perform various functions.

Input/output (“I/O”) module may include connectivity to a microphone,keyboard, touch screen, mouse, and/or stylus through which a user ofcomputer 101 may provide input. The input may include input relating tocursor movement. The input may relate to language processing. Theinput/output module may also include one or more speakers for providingaudio output and a video display device for providing textual, audio,audiovisual, and/or graphical output. The input and output may berelated to computer application functionality. The input and output maybe related to user requests from a system.

System 100 may be connected to other systems via a local area network(LAN) interface 113.

System 100 may operate in a networked environment supporting connectionsto one or more remote computers, such as terminals 141 and 151.Terminals 141 and 151 may be personal computers or servers that includemany or all of the elements described above relative to system 100. Thenetwork connections depicted in FIG. 1 include a local area network(LAN) 125 and a wide area network (WAN) 129, but may also include othernetworks. When used in a LAN networking environment, computer 101 isconnected to LAN 125 through a LAN interface or adapter 113. When usedin a WAN networking environment, computer 101 may include a modem 127 orother means for establishing communications over WAN 129, such asInternet 131.

It will be appreciated that the network connections shown areillustrative and other means of establishing a communications linkbetween computers may be used. The existence of various well-knownprotocols such as TCP/IP, Ethernet, FTP, HTTP and the like is presumed,and the system can be operated in a client-server configuration topermit a user to retrieve web pages from a web-based server. Theweb-based server may transmit data to any other suitable computersystem. The web-based server may also send computer-readableinstructions, together with the data, to any suitable computer system.The computer-readable instructions may be to store the data in cachememory, the hard drive, secondary memory, or any other suitable memory.

Additionally, application program(s) 119, which may be used by computer101, may include computer executable instructions for invoking userfunctionality related to communication, such as e-mail, Short MessageService (SMS), and voice input and speech recognition applications.Application program(s) 119 (which may be alternatively referred toherein as “plugins,” “applications,” or “apps”) may include computerexecutable instructions for invoking user functionality relatedperforming various tasks. The various tasks may be related to languageprocessing of user requests.

Computer 101 and/or terminals 141 and 151 may also be devices includingvarious other components, such as a battery, speaker, and/or antennas(not shown).

Terminal 151 and/or terminal 141 may be portable devices such as alaptop, cell phone, Blackberry™, tablet, smartphone, or any othersuitable device for receiving, storing, transmitting and/or displayingrelevant information. Terminals 151 and/or terminal 141 may be otherdevices. These devices may be identical to system 100 or different. Thedifferences may be related to hardware components and/or softwarecomponents.

Any information described above in connection with database 111, and anyother suitable information, may be stored in memory 115. One or more ofapplications 119 may include one or more algorithms that may be used toimplement features of the disclosure, and/or any other suitable tasks.

The invention may be operational with numerous other general purpose orspecial purpose computing system environments or configurations.Examples of well-known computing systems, environments, and/orconfigurations that may be suitable for use with the invention include,but are not limited to, personal computers, server computers, hand-heldor laptop devices, tablets, mobile phones, smart phones and/or otherpersonal digital assistants (“PDAs”), multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, network PCs, minicomputers, mainframe computers,distributed computing environments that include any of the above systemsor devices, and the like.

The invention may be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by a computer. Generally, program modules include routines,programs, objects, components, data structures, etc., that performparticular tasks or implement particular abstract data types. Theinvention may also be practiced in distributed computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications network. In a distributed computingenvironment, program modules may be located in both local and remotecomputer storage media including memory storage devices.

FIG. 2 shows illustrative apparatus 200 that may be configured inaccordance with the principles of the disclosure. Apparatus 200 may be acomputing machine. Apparatus 200 may include one or more features of theapparatus shown in FIG. 1. Apparatus 200 may include chip module 202,which may include one or more integrated circuits, and which may includelogic configured to perform any other suitable logical operations.

Apparatus 200 may include one or more of the following components: I/Ocircuitry 204, which may include a transmitter device and a receiverdevice and may interface with fiber optic cable, coaxial cable,telephone lines, wireless devices, PHY layer hardware, a keypad/displaycontrol device or any other suitable media or devices; peripheraldevices 206, which may include counter timers, real-time timers,power-on reset generators or any other suitable peripheral devices;logical processing device 208, which may compute data structuralinformation and structural parameters of the data; and machine-readablememory 210.

Machine-readable memory 210 may be configured to store inmachine-readable data structures: machine executable instructions (whichmay be alternatively referred to herein as “computer instructions” or“computer code”), applications, signals, and/or any other suitableinformation or data structures.

Components 202, 204, 206, 208 and 210 may be coupled together by asystem bus or other interconnections 212 and may be present on one ormore circuit boards such as 220. In some embodiments, the components maybe integrated into a single chip. The chip may be silicon-based.

FIG. 3 shows illustrative system architecture 300 in accordance withprinciples of the disclosure. System architecture 300 may include tiers1-4. Tier 1 may include module 301, which may be the root node. Tier 2may include modules 303-307. Tier 3 may include modules 309-317. Tier 4may include modules 319 and 321. The solid lines connecting the modulesshow a parent/child relationship. Parents may be in higher tiers thanchildren. For example, modules 319 and 321 may be children of module309.

FIG. 4 shows illustrative system diagram 400 in accordance withprinciples of the disclosure. Diagram 400 includes platform 401.Platform 401 may include processor 403 and database 405. Processor 403may include, or be linked to, machine learning and/or languageprocessing engines. Database 405 may include regular expression patternsets that may be associated with various modules. Platform 401 may belinked to user devices 407-411. Illustrative user devices may includetelephones and computing devices.

FIG. 5 shows illustrative flowchart 500 in accordance with principles ofthe disclosure. Flowchart 500 begins with receiving an utterance at 503.The system may tokenize the utterance at 505. The system may annotatethe utterance at 507. The system may match the processed utterance at509. If the match is the highest tier match at 511, the system may checkfor descendants at 513. If no descendants exist, the system may compilethe matching highest tier into a result set at 515. If descendants doexist, the system may compile the descendants into a result set at 517.The system may filter the result set at 519, and transmit the set to theuser at 521.

The steps of methods may be performed in an order other than the ordershown and/or described herein. Embodiments may omit steps shown and/ordescribed in connection with illustrative methods. Embodiments mayinclude steps that are neither shown nor described in connection withillustrative methods.

Illustrative method steps may be combined. For example, an illustrativemethod may include steps shown in connection with another illustrativemethod.

Apparatus may omit features shown and/or described in connection withillustrative apparatus. Embodiments may include features that areneither shown nor described in connection with the illustrativeapparatus. Features of illustrative apparatus may be combined. Forexample, an illustrative embodiment may include features shown inconnection with another illustrative embodiment.

The drawings show illustrative features of apparatus and methods inaccordance with the principles of the invention. The features areillustrated in the context of selected embodiments. It will beunderstood that features shown in connection with one of the embodimentsmay be practiced in accordance with the principles of the inventionalong with features shown in connection with another of the embodiments.

One of ordinary skill in the art will appreciate that the steps shownand described herein may be performed in other than the recited orderand that one or more steps illustrated may be optional. The methods ofthe above-referenced embodiments may involve the use of any suitableelements, steps, computer-executable instructions, or computer-readabledata structures. In this regard, other embodiments are disclosed hereinas well that can be partially or wholly implemented on acomputer-readable medium, for example, by storing computer-executableinstructions or modules or by utilizing computer-readable datastructures.

Thus, methods and systems for a hierarchical multi-tier languageprocessing platform are provided. Persons skilled in the art willappreciate that the present invention can be practiced by other than thedescribed embodiments, which are presented for purposes of illustrationrather than of limitation, and that the present invention is limitedonly by the claims that follow.

What is claimed is:
 1. A hierarchical multi-tier digital platform withincreased processing speed, accuracy, and efficiency for routing averbal request, said platform comprising: a processor; a databasecomprising a plurality of modules, each module comprising a set ofinformation, wherein said plurality of modules is configured in amulti-tier tree architecture, said multi-tier tree architecturecomprising at least three tiers, wherein: the first tier comprises afirst module that is a root node; each tier aside from the first tiercomprises one or more modules that are each a child of a parent modulethat is one tier up in the tree, wherein up in the tree is closer to thefirst tier; and the set of information of a child module is a subset ofthe set of information of the parent module of said child module; and anon-transitory memory storing a set of computer-executable instructions,that, when run on the processor, are configured to: receive anutterance, said utterance comprising a string of one or more wordsspoken by a system user, said string of words comprising a user request;process the utterance via a natural language processing (NLP) engine;and route the utterance, said routing comprising: identifying a highesttier module that matches a predetermined portion of the utterance; whenat least one descendent module of said highest tier module exists,compiling one or more of the descendent modules into a result set ofmodules; when descendent modules of said highest tier module do notexist, compiling said highest tier module into the result set ofmodules; and transmitting the result set of modules to the system user,the result set of modules comprising a comprehensive and narrowlytailored response to the user request; wherein the database furthercomprises at least one rule set, said rule set comprising a set ofregular expression patterns, and the platform is further configured toresolve an intent of the utterance by comparing the utterance to therule set to find a match between the utterance and one of the regularexpression patterns; and wherein the set of regular expression patternsof one of the rule sets comprises: ([/range|quote|close|open/][/for|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/reports|price/][/range/]? [/for|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/sector/][/for|to/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/52-week/] [/range/][/for|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/analysts/])(?$StockName[{tag:/NN.*|FW|VBP/}]{1,4}); ([/nav/])(?$StockName[{tag:/NN.*|FW|VBP/}]{1,4});([/peers|volume|esg|analysts|articles|beta|coverage|dividend|earnings|eps|nav|news|fundamentals|chart|chain/] [/for|to|of/]?)(?$StockName[{tag:/NN.*|FW|VBP/}]{1,4}); ([/cfra|lipper|morningstar/]?[/rating|ratings/] [/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4});([/day|day's/] [/'s/]? [/high|low|change/] [/for|to|of/]?)(?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/market/]? [/cap/][/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/price/][/objective/] [/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,43);([/news/] [/wire/]? [/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4});([/expense|price|p\/e/] [/ratio/]? [/for|to|of/]?)(?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/earnings/] [/per/]? [/share/]?[/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,43); ([/fund/][/inception/] [/date/] [/for|to|of/]?)(?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/carbon/] [/footprint/][/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/net/] [/asset/][/value/] [/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4});(?$StockName[{tag:/NN.*|FW/}]{1,4}) ([/quote|chart|trends|doing/]);(?$StockName[{tag:/NN.*|FW/}]{1,4}) ([/stock|fund/] [/story/]?);([/stock|fund|quote|chart|trends|impact/] [/for|to|of/]?)(?$StockName[{tag:/NN.*|FW/}]{1,4}); and ([/sell|buy|add|remove|short/][/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4});

wherein NM refers to a word that is associated with being a noun; and FWrefers to a foreign word, said foreign word being a word that is notrecognized as a word that is part of a predetermined native language. 2.The platform of claim 1, wherein the result set of modules comprisesonly the descendent modules that are a maximum of one tier below saidhighest tier module.
 3. The platform of claim 2, wherein, when theresult set of modules comprises at least one module that is a parentmodule to at least two child modules, the platform is further configuredto transmit to the system user a request to select one or more modulesfrom the at least two child modules, and, based on the selection, removefrom the result set any unselected modules of the at least two childmodules.
 4. The platform of claim 1, wherein the result set of modulescomprises only modules that are leaf nodes, said leaf nodes beingmodules that are not parent modules of any child modules.
 5. Theplatform of claim 1, wherein the system user is subscribed to one ormore of the plurality of modules, and the result set of modules isfiltered to include only subscribed modules, said subscribed modulescomprising modules to which the system user is subscribed or to whichthe system user is subscribed to at least one descendent.
 6. Theplatform of claim 1, wherein each module comprises an investment accountor a category of investment accounts, and the set of information of amodule comprises investment account information.
 7. The platform ofclaim 1, wherein the database comprises a distinct rule set for eachmodule.
 8. The platform of claim 1, wherein one of the rule setscomprises a rule for identifying stock names, and said rule foridentifying stock names comprises identifying a word from the utterancethat is a noun as a stock name, wherein a word that is a noun includes aword that is a part of speech other than a noun but is used in a portionof the utterance that is associated with being a noun.
 9. The platformof claim 1, wherein processing the utterance comprises: tokenizing theutterance via the NLP engine; and annotating the utterance via the NLPengine.
 10. A method for routing a verbal request with increasedprocessing speed, accuracy, and efficiency, said method executed via aset of computer-executable instructions stored in a non-transitorymemory and run on a processor, said method comprising: storing aplurality of modules in a database, each module comprising a set ofinformation; configuring said plurality of modules in a multi-tier treearchitecture, said multi-tier tree architecture comprising at leastthree tiers, wherein: the first tier comprises a first module that is aroot node; each tier aside from the first tier comprises one or moremodules that are each a child of a parent module that is one tier up inthe tree, wherein up in the tree is closer to the first tier; and theset of information of a child module is a subset of the set ofinformation of the parent module of said child module; receiving anutterance, said utterance comprising a string of one or more wordsspoken by a system user, said string of words comprising a user request;processing the utterance via a natural language processing (NLP) engine;and routing the utterance, said routing comprising: identifying ahighest tier module that matches a predetermined portion of theutterance; when at least one descendent module of said highest tiermodule exists, compiling one or more of the descendent modules into aresult set of modules; when descendent modules of said highest tiermodule do not exist, compiling said highest tier module into the resultset of modules; and transmitting the result set of modules to the systemuser, the result set of modules comprising a comprehensive and narrowlytailored response to the user request; wherein the database furthercomprises at least one rule set, said rule set comprising a set ofregular expression patterns, and the method further comprises resolvingan intent of the utterance by comparing the utterance to the rule set tofind a match between the utterance and one of the regular expressionpatterns; and one of the rule sets comprises a rule for identifyingstock names, and said rule for identifying stock names comprisesidentifying a word from the utterance that is a noun as a stock name,wherein a word that is a noun includes a word that is a part of speechother than a noun but is used in a portion of the utterance that isassociated with being a noun; and wherein the database comprises adistinct rule set for each module; and the set of regular expressionpatterns of one of the rule sets comprises: ([/range|quote|close|open/][/for|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/reports|price/][/range/]? [/for|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/sector/][/for|to/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/52-week/] [/range/][/for|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/analysts/])(?$StockName[{tag:/NN.*|FW|VBP/}]{1,4}); ([/nav/])(?$StockName[{tag:/NN.*|FW|VBP/}]{1,4});([/peers|volume|esg|analysts|articles|beta|coverage|dividend|earnings|eps|nav|news|fundamentals|chart|chain/] [/for|to|of/]?)(?$StockName[{tag:/NN.*|FW|VBP/}]{1,4}); ([/cfra|lipper|morningstar/]?[/rating|ratings/] [/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4});([/day|day's/] [/'s/]? [/high|low|change/] [/for|to|of/]?)(?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/market/]? [/cap/][/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/price/][/objective/] [/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,43);([/news/] [/wire/]? [/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4});([/expense|price|p\/e/] [/ratio/]? [/for|to|of/]?)(?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/earnings/] [/per/]? [/share/]?[/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,43); ([/fund/][/inception/] [/date/] [/for|to|of/]?)(?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/carbon/] [/footprint/][/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/net/] [/asset/][/value/] [/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4});(?$StockName[{tag:/NN.*|FW/}]{1,4}) ([/quote|chart|trends|doing/]);(?$StockName[{tag:/NN.*|FW/}]{1,4}) ([/stock|fund/] [/story/]?);([/stock|fund|quote|chart|trends|impact/] [/for|to|of/]?)(?$StockName[{tag:/NN.*|FW/}]{1,4}); and ([/sell|buy|add|remove|short/][/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4});

wherein NN refers to a word that is associated with being a noun; and FWrefers to a foreign word, said foreign word being a word that is notrecognized as a word that is part of a predetermined native language.11. The method of claim 10, wherein: the result set of modules comprisesonly the descendent modules that are a maximum of one tier below saidhighest tier module; and when the result set of modules comprises atleast one module that is a parent module to at least two child modules,the method further comprises transmitting to the system user a requestto select one or more modules from the at least two child modules, and,based on the selection, removing from the result set any unselectedmodules of the at least two child modules.
 12. The method of claim 10,wherein the result set of modules comprises only modules that are leafnodes, said leaf nodes being modules that are not parent modules of anychild modules.
 13. The method of claim 10, wherein the system user issubscribed to one or more of the plurality of modules, and the resultset of modules is filtered to include only subscribed modules, saidsubscribed modules comprising modules to which the system user issubscribed or to which the system user is subscribed to at least onedescendent.
 14. The method of claim 10, wherein each module comprises aninvestment account or a category of investment accounts, and the set ofinformation of a module comprises investment account information. 15.The method of claim 10, wherein processing the utterance comprises:tokenizing the utterance via the NLP engine; and annotating theutterance via the NLP engine.
 16. A language processing system withincreased processing speed, accuracy, and efficiency, said systemcomprising: a processor; a database comprising a plurality of modules,each module comprising a set of information, wherein said plurality ofmodules is configured in a multi-tier tree architecture, said multi-tiertree architecture comprising at least three tiers, wherein: the firsttier comprises a first module that is a root node; each tier aside fromthe first tier comprises one or more modules that are each a child of aparent module that is one tier up in the tree, wherein up in the tree iscloser to the first tier; and the set of information of a child moduleis a subset of the set of information of the parent module of said childmodule; and a non-transitory memory storing a set of computer-executableinstructions, that, when run on the processor, are configured to:receive an utterance, said utterance comprising a string of one or morewords spoken by a system user, said string of words comprising a userrequest; process the utterance via a natural language processing (NLP)engine; and route the utterance, said routing comprising: identifying ahighest tier module that matches a predetermined portion of theutterance; when at least one descendent module of said highest tiermodule exists, compiling one or more of the descendent modules into aresult set of modules; when descendent modules of said highest tiermodule do not exist, compiling said highest tier module into the resultset of modules; and transmitting the result set of modules to the systemuser, the result set of modules comprising a comprehensive and narrowlytailored response to the user request; wherein: the result set ofmodules comprises only modules that are leaf nodes, said leaf nodesbeing modules that are not parent modules of any child modules; and thesystem user is subscribed to one or more of the plurality of modules,and the result set of modules is filtered to include only subscribedmodules, said subscribed modules comprising modules to which the systemuser is subscribed or to which the system user is subscribed to at leastone descendent; wherein the database further comprises at least one ruleset, said rule set comprising a set of regular expression patterns, andthe system is further configured to resolve an intent of the utteranceby comparing the utterance to the rule set to find a match between theutterance and one of the regular expression patterns; and one of therule sets comprises a rule for identifying stock names, and said rulefor identifying stock names comprises identifying a word from theutterance that is a noun as a stock name, wherein a word that is a nounincludes a word that is a part of speech other than a noun but is usedin a portion of the utterance that is associated with being a noun; andwherein the database comprises a distinct rule set for each module; andthe set of regular expression patterns of one of the rule setscomprises: ([/range|quote|close|open/] [/forjof/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/reports|price/] [/range/]? [/for|of/]?)(?$StockName[ {tag:/NN.*|FW/}]{1,4}); ([/sector/] [/for|to/]?)(?$StockName[ {tag:/NN.*|FW/}]{1,4}); ({/52-week/] [/range/][/for|of/]?) (?$StockName[ {tag:/NN.*|FW/}]{1,4}); ([/analysts/)(?$StockName[ {tag:/NN.*|FW/}]{1,4}); ([/nav/])(?$StockName[{tag:/NN.*|FW/}]{1,4});([/peers|volume|esg|analysts|articles|beta|coverage|dividend|earnings|eps|nav|news|fundamen tals| chart|chain/] [/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4});([/cfral|lipper|morningstar/]?[/rating|ratings/]/for|to|of/]?)(?$StockName [{tag:/NN.*|FW/}]{1,4}); ):([/day|day's/][/'s]?[/high|low|change/][/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}] {1,4}); ([/market/]?[/cap/] [/for|to|of/]?)(?$StockName| {tag:/NN.*|FW/}]{1,4}); ([/price/][/objective][/for|to|of/]?) (?$StockName[{tag:/NN.*|FW/}]{1,4}); ([/news/] [/wire/]?[/for|to|of/]?) (?$StockName[ {tag:/NN.*|FW/}]{1,4});([/expense|price|pVe/] [/ratio/]? [/for|to|of/]?)(?$StockName|[{tag:/NN.*|FW/}]{1,4}); ({/earnings/] [/per/]? [/share/]?[/for|to|of/]?) (?$StockName|[ {tag:/NN.*|FW/}]{1,4}); ({/fund/][/inception/] [/date/] [/for|to|of/]?) (°$StockName|[{tag:/NN.*|FW/}]{1,4}); ({/carbon/] [/footprint/] [/for|to|of/]?)(?$StockName|[{tag:/NN.*|FW/}]{1,4}); ([/net/] [/asset/] [/value/][/for|to|of/]?) (?$StockName[ {tag:/NN.*|FW/}]{1,4}); (?$StockName|{tag:/NN.*|FW/}]{1,4}) ([/quote|chat|trends|doing/]); (?$StockName|{tag:/NN.*|FW/}]{1,4}) ([/stock|fund/] [/story/]?);({/stock|fund|quote|chart|trends|impact/] [/for|to|of/]?) (?$StockName|[{tag:/NN.*|FW/}]{1,4}); and ([{/sell|buy|add|remove|short/][/for|to|of/]?) (?$StockName|[{tag:/NN.*|FW/}] {1,4});

wherein NN refers to a word that is associated with being a noun; and FWrefers to a foreign word, said foreign word being a word that is notrecognized as a word that is part of a predetermined native language.